Data collection ran from December 11, 2024, to January 30, 2025, and achieved a 59 percent response rate. The questionnaire covered four domains: perceptions of artificial intelligence in healthcare, self-reported readiness to use it, educational needs, and barriers to integrating it into the curriculum. Researchers analyzed the responses with descriptive statistics and exploratory ordinary least squares regression to identify factors linked to a composite readiness score.

The results reveal a gap between attitude and ability. Students generally agreed that artificial intelligence can support clinical decision-making, and most viewed it as an assistant to physicians rather than a substitute. At the same time, they voiced concerns about bias and unintended consequences, reflecting a cautious but receptive stance toward the technology.

That receptiveness was not matched by confidence. When asked how ready they felt to use artificial intelligence in key clinical tasks, only between 2 and 15 percent of respondents reported high readiness, with the figure varying by task. Familiarity with healthcare-specific applications of artificial intelligence remained low across the cohort.

Half of the students reported only minimal exposure to the field and 36 percent reported moderate exposure, while 50 percent had received some formal education on the topic. The regression analysis pointed to one factor that mattered. Familiarity with healthcare applications of artificial intelligence was the only significant predictor of readiness, with a coefficient of 0.428 and a p-value below 0.001.

Notably, prior general exposure to artificial intelligence was not associated with readiness, and readiness remained low across all four years of training. This suggests that simply encountering artificial intelligence tools is not enough; students need exposure specifically tied to healthcare contexts to feel prepared to use them. Students were clear about what they wanted: hands-on, clinically relevant, and ethically grounded training, with particular interest in artificial intelligence fundamentals and its clinical applications.

Three barriers dominated the responses. Limited curricular time was cited by 63 percent of students, ethical concerns by 51 percent, and restricted access to relevant tools by 49 percent. The authors conclude that integrating structured, clinically relevant, and longitudinal training in artificial intelligence into Rural Medical Education curricula may better prepare future rural physicians to use the technology responsibly and effectively.

As a first assessment of this specific student population in the United States, the study has inherent limitations. It reflects a single program at one institution, relies on self-reported readiness rather than objective skill testing, and captures a snapshot in time from a modest sample of 88 students with a 59 percent response rate. The regression analysis was exploratory, so the identified association between familiarity and readiness should be treated as hypothesis-generating rather than proof of a causal effect.

Even so, the survey provides a concrete baseline for medical educators weighing how, and how quickly, to build artificial intelligence training into curricula designed for the physicians who will serve rural communities.

Subject of Research: Artificial intelligence literacy, readiness, and training needs among U.S. rural medical education students

Article Title: Artificial intelligence literacy among U.S. rural medical education students: perceptions, readiness, educational needs, and barriers

Article References: Balakrishnan, K., Li, Z., Adkinson, B. D., Abuhay, T. M., & Hinkle, H. E. (2026). Artificial intelligence literacy among U.S. rural medical education students: perceptions, readiness, educational needs, and barriers. BMC Medical Education. https://doi.org/10.1186/s12909-026-10158-1

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10158-1

Keywords: artificial intelligence, medical education, rural health, cross-sectional survey, clinical decision-making, curriculum barriers, health literacy, physician training, BMC Medical Education, AI readiness, Artificial, intelligence